Spectral imaging combines both imaging and spectroscopy. In general, imaging technology can provide intensity information at points in an image plane I(x, y), where (x, y) designates the location on the image plane. A typical spectrometer, as used in spectroscopy, can provide a single spectrum I(λ), i.e., intensity information at each wavelength λ (spectral component). In comparison, spectral imaging can provide a spectral image I(x, y, λ) that includes spectral intensity at each point in the image plane. Therefore, a spectral image can be viewed as a cube of information (also referred to as a datacube) represented by a three-dimensional (3D) data set, where two dimensions represent spatial coordinates (e.g., x and y) and the third dimension represents a spectral coordinate (e.g., λ). Conventionally, a spectral image I(x, y, λ) can be decomposed into either a collection of many images, each of which is measured at a distinct wavelength, or into a collection of pixels, each of which includes a detailed spectral curve.
Unlike conventional color imaging (e.g., with a color camera), which uses only three primary colors, spectral imaging can utilize many more color channels, thereby improving the color differentiation capabilities. In addition, spectral imaging can reach spectral regimes that might not be accessible to conventional color imaging, such as the infrared or Terahertz (THz) regime. Therefore, spectral imaging can be useful when precise spectral or color information is desirable (e.g., to distinguish between similarly colored objects or measure overlapping fluorescent signals) or when an image of the scene is desired (e.g., if the object of interest is not uniformly one color, the area of interest contains multiple objects, or scenes in which a single-point spectrometer would not accurately sample or cover the area of interest).
Spectral images can be acquired via several methods. One method of spectral imaging uses a set of narrow band filters, each of which can transmit light around a particular wavelength (e.g., within a bandwidth of 10 nm). This method captures a full spectral image by measuring one image at a time but each time at a different wavelength.
A similar method can use a variable-filter, such as a circular-variable filter (CVF), liquid-crystal tunable filter (LCTF), or acousto-optical tunable filter (AOTF). A CVF can include a thin film deposited on a circular substrate. The film thickness and therefore the wavelength of the CVF's peak transmittance can vary linearly and continuously with respect to angular position on the substrate, therefore allowing transmission of a narrow-band light as a function of the incident beam location on the filter.
The LCTF and AOTF are generally narrowband electro-optical filters with no moving parts. One example of a LCTF (Lyot design) includes polarizable liquid crystal mounted between two linear polarizers. Varying the voltage applied to the liquid crystal tunes the filter passband. An AOTF can use an acousto-optic crystal, such as Tellurium dioxide (TeO2), in which acoustic waves propagate. The acousto-optic crystal deforms to form a grating structure that mimics the acoustic waves and transmits or reflects incident light. The specific period of the grating structure and therefore the transmission wavelength of the AOTF can depend on the frequency of the acoustic waves. A common issue with using narrow-band filters is the low optical signal throughput, as a large fraction of light is rejected by filters at any given time.
Another method of spectral imaging uses a grating or a prism to disperse incident light from an object to be imaged so as to acquire hyperspectral information simultaneously on a single imaging pixel array. This method may offer improved signal throughput (also referred to as “snapshot advantage”). However, the spatial resolution and signal-to-noise ratio (SNR) in this method can be limited because the detector pixel arrays are normally divided into sub-arrays, each of which captures only one wave band. The amount of light incident on the pixels can be reduced by spectrum splitting, thereby reducing the SNR, especially in the mid-wave and long-wave infrared.
A third method of spectral imaging can utilize a superposition of the spectral or spatial information and derive the spectral image via the transformation of the acquired data. One example of this method is Fourier spectroscopy, in which spectrum can be measured from the interference of light. An interferometer can split a beam of light into two beams, which are recombined and interfere at the detector, thereby generating interferograms as a function of spectrum. Fourier-transforming the interferograms yields the spectrum. Fourier Transform Infrared (FTIR) cameras may mitigate the SNR issue by capitalizing on the Fellgett/multiplex advantage in spectroscopy. However, these cameras normally also use fragile opto-mechanical moving parts (e.g., scanning interferometers) that may decrease the system robustness and increase the cost (e.g., an FTIR hyperspectral camera from Telops Inc. costs upwards of $750,000).